TY - GEN
T1 - Improved multi-label classification using inter-dependence structure via a generative mixture model
AU - Simha, Ramanuja
AU - Shatkay, Hagit
N1 - Publisher Copyright:
© 2016 The Authors and IOS Press.
PY - 2016
Y1 - 2016
N2 - Single-label classification associates each instance with a single label, while multi-label classification (MLC), assigns multiple labels to instances. Simple MLC systems assume that labels are independent of one another, while more complex approaches capture inter-dependencies among labels. Experiments comparing performance of MLC systems demonstrate that there is much room for improvement. Notably, when an instance is associated with multiple labels, a feature-value of the instance may depend only on a subset of these labels and thus be conditionally independent of the others given the label-subset. Current systems do not account for such conditional independence. Moreover, dependence of a feature-value on a label is likely to imply its dependence on other inter-dependent labels. Our hypothesis is that by explicitly modeling the dependence between feature values and specific subsets of inter-dependent labels, the assignment of multi-labels to instances can be done more accurately. We present a probabilistic generative model that captures dependencies among labels as well as between features and labels, by means of a Bayesian network. We introduce the concept of label dependency sets as a basis for a new mixture model that represents conditional independencies between features and labels given subsets of inter-dependent labels. Experimental results show that the performance of the system we have developed based on our model for MLC significantly improves upon results obtained by current MLC systems that are based on probabilistic models.
AB - Single-label classification associates each instance with a single label, while multi-label classification (MLC), assigns multiple labels to instances. Simple MLC systems assume that labels are independent of one another, while more complex approaches capture inter-dependencies among labels. Experiments comparing performance of MLC systems demonstrate that there is much room for improvement. Notably, when an instance is associated with multiple labels, a feature-value of the instance may depend only on a subset of these labels and thus be conditionally independent of the others given the label-subset. Current systems do not account for such conditional independence. Moreover, dependence of a feature-value on a label is likely to imply its dependence on other inter-dependent labels. Our hypothesis is that by explicitly modeling the dependence between feature values and specific subsets of inter-dependent labels, the assignment of multi-labels to instances can be done more accurately. We present a probabilistic generative model that captures dependencies among labels as well as between features and labels, by means of a Bayesian network. We introduce the concept of label dependency sets as a basis for a new mixture model that represents conditional independencies between features and labels given subsets of inter-dependent labels. Experimental results show that the performance of the system we have developed based on our model for MLC significantly improves upon results obtained by current MLC systems that are based on probabilistic models.
UR - http://www.scopus.com/inward/record.url?scp=85013074928&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85013074928&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-672-9-1336
DO - 10.3233/978-1-61499-672-9-1336
M3 - Conference contribution
AN - SCOPUS:85013074928
T3 - Frontiers in Artificial Intelligence and Applications
SP - 1336
EP - 1343
BT - Frontiers in Artificial Intelligence and Applications
A2 - Kaminka, Gal A.
A2 - Fox, Maria
A2 - Bouquet, Paolo
A2 - Hullermeier, Eyke
A2 - Dignum, Virginia
A2 - Dignum, Frank
A2 - van Harmelen, Frank
PB - IOS Press BV
T2 - 22nd European Conference on Artificial Intelligence, ECAI 2016
Y2 - 29 August 2016 through 2 September 2016
ER -